Embedded Machine Learning - Mdhaffar, Afef
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This book represents a first step towards embedded machine learning. It presents techniques for optimizing and compressing deep learning models. These techniques make it easier to deploy a high-performance, lightweight deep learning model on resource-constrained devices such as smartphones and microcontrollers. This paper also explores a topical knowledge transfer technique, namely knowledge distillation. This technique makes it possible to improve the performance of a lightweight deep learning model, while transferring to it the knowledge of a complex, high-performance deep learning model.…mehr

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Produktbeschreibung
This book represents a first step towards embedded machine learning. It presents techniques for optimizing and compressing deep learning models. These techniques make it easier to deploy a high-performance, lightweight deep learning model on resource-constrained devices such as smartphones and microcontrollers. This paper also explores a topical knowledge transfer technique, namely knowledge distillation. This technique makes it possible to improve the performance of a lightweight deep learning model, while transferring to it the knowledge of a complex, high-performance deep learning model. All these techniques have been detailed in this book and illustrated with practical Python implementations, generally based on the use of the pytorch and tensorflow libraries.
Autorenporträt
Afef Mdhaffar received her PhD in Computer Science in 2014 from the University of Marburg, Germany. She is currently a lecturer in computer science at the École Nationale d'Ingénieurs de Sfax (ENIS), Tunisia. Her current work focuses on deep learning model optimization techniques.